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Annual cycle of energy allocation to growth and reproduction of yellow perch

2000· article· en· W1976239210 on OpenAlexaff
Bryan A. Henderson, Tanya Vinodrai Trivedi, Nicholas C. Collins

Bibliographic record

VenueJournal of Fish Biology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsAurora CollegeUniversity of Toronto
Fundersnot available
KeywordsBiologyFecundityReproductionBioenergeticsAnimal sciencePerchGonadAllometryOvaryReproductive cycleDevelopment of the gonadsEcologyZoologyFisheryFish <Actinopterygii>AnatomyEndocrinologyDemography

Abstract

fetched live from OpenAlex

Growth (length, weight) of yellow perch Perca flavescens in western and west‐central Lake Erie began between June and July, possibly reaching asymptotic size by early winter. Energy density (kJ g −1 ) of somatic tissue increased markedly from June to September, then declined to low levels by the following spring. Consequently, energy mass (kJ) of somatic tissue increased to September and then declined slowly until March. Ovarian growth began in October, and the size of the ovaries was maximal in April, just before spawning in May. The energy density of ovaries, however, increased to a peak in December, and then declined. The increase in ovary size in the winter, therefore, resulted largely from an allocation of tissue of low energy density. Fecundity and gonad size were correlated with somatic and gonadal energy density. A bioenergetics model was used to describe the monthly allocations of energy to respiration, growth, excretion, and reproduction. Most direct reproductive costs (80%) were incurred from September to December, corresponding to the cycle in the activity of the liver. Energy acquired in the early summer may be critical for determining maturation, fecundity, and egg quality. From 1978 to 1990, &lt;80% of the females from the western and west‐central basins of Lake Erie were classed as spent in July.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.215
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations94
Published2000
Admission routes1
Has abstractyes

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